{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HW-3: Problem 2\n",
    "\n",
    "This code is used to solve Problem 2 in HW 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as sm\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABC</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADI</th>\n",
       "      <th>ADM</th>\n",
       "      <th>...</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZION</th>\n",
       "      <th>MKT</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>CPI</th>\n",
       "      <th>IndProd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>8.965403</td>\n",
       "      <td>5.617027</td>\n",
       "      <td>1.604202</td>\n",
       "      <td>-15.559467</td>\n",
       "      <td>4.947367</td>\n",
       "      <td>7.786493</td>\n",
       "      <td>7.793523</td>\n",
       "      <td>0.397299</td>\n",
       "      <td>3.687699</td>\n",
       "      <td>4.077818</td>\n",
       "      <td>...</td>\n",
       "      <td>3.016765</td>\n",
       "      <td>-1.707844</td>\n",
       "      <td>11.253543</td>\n",
       "      <td>8.592044</td>\n",
       "      <td>5.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.281588</td>\n",
       "      <td>0.405392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>-7.655023</td>\n",
       "      <td>-6.136895</td>\n",
       "      <td>3.763957</td>\n",
       "      <td>-2.577849</td>\n",
       "      <td>4.403527</td>\n",
       "      <td>-0.265997</td>\n",
       "      <td>3.378099</td>\n",
       "      <td>3.837650</td>\n",
       "      <td>4.302967</td>\n",
       "      <td>11.621178</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.140784</td>\n",
       "      <td>0.828099</td>\n",
       "      <td>0.481413</td>\n",
       "      <td>3.537714</td>\n",
       "      <td>1.29</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.209016</td>\n",
       "      <td>-0.172034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>1.459880</td>\n",
       "      <td>23.395607</td>\n",
       "      <td>8.016544</td>\n",
       "      <td>0.285049</td>\n",
       "      <td>8.621657</td>\n",
       "      <td>4.428448</td>\n",
       "      <td>2.142036</td>\n",
       "      <td>10.162677</td>\n",
       "      <td>2.769777</td>\n",
       "      <td>5.703634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.217944</td>\n",
       "      <td>9.408769</td>\n",
       "      <td>0.615394</td>\n",
       "      <td>3.419136</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.78</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041407</td>\n",
       "      <td>0.109350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>-1.270848</td>\n",
       "      <td>-0.413346</td>\n",
       "      <td>1.477238</td>\n",
       "      <td>0.027105</td>\n",
       "      <td>5.059334</td>\n",
       "      <td>4.805337</td>\n",
       "      <td>7.999121</td>\n",
       "      <td>3.533298</td>\n",
       "      <td>-5.527490</td>\n",
       "      <td>0.620661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687039</td>\n",
       "      <td>-4.955616</td>\n",
       "      <td>1.621978</td>\n",
       "      <td>-1.491662</td>\n",
       "      <td>1.55</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.237758</td>\n",
       "      <td>0.203532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>9.236622</td>\n",
       "      <td>3.887928</td>\n",
       "      <td>-2.853882</td>\n",
       "      <td>2.224061</td>\n",
       "      <td>0.311349</td>\n",
       "      <td>-0.679443</td>\n",
       "      <td>0.819326</td>\n",
       "      <td>-4.933379</td>\n",
       "      <td>5.058140</td>\n",
       "      <td>-4.613367</td>\n",
       "      <td>...</td>\n",
       "      <td>1.797858</td>\n",
       "      <td>-0.544640</td>\n",
       "      <td>2.658907</td>\n",
       "      <td>13.259207</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.195554</td>\n",
       "      <td>-0.427080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        AAL       AAP       AAPL       ABC       ABT  \\\n",
       "Date                                                                       \n",
       "2013-01-31  8.965403   5.617027  1.604202 -15.559467  4.947367  7.786493   \n",
       "2013-02-28 -7.655023  -6.136895  3.763957  -2.577849  4.403527 -0.265997   \n",
       "2013-03-31  1.459880  23.395607  8.016544   0.285049  8.621657  4.428448   \n",
       "2013-04-30 -1.270848  -0.413346  1.477238   0.027105  5.059334  4.805337   \n",
       "2013-05-31  9.236622   3.887928 -2.853882   2.224061  0.311349 -0.679443   \n",
       "\n",
       "                 ACN       ADBE       ADI        ADM  ...       XYL       YUM  \\\n",
       "Date                                                  ...                       \n",
       "2013-01-31  7.793523   0.397299  3.687699   4.077818  ...  3.016765 -1.707844   \n",
       "2013-02-28  3.378099   3.837650  4.302967  11.621178  ... -1.140784  0.828099   \n",
       "2013-03-31  2.142036  10.162677  2.769777   5.703634  ...  0.217944  9.408769   \n",
       "2013-04-30  7.999121   3.533298 -5.527490   0.620661  ...  0.687039 -4.955616   \n",
       "2013-05-31  0.819326  -4.933379  5.058140  -4.613367  ...  1.797858 -0.544640   \n",
       "\n",
       "                  ZBH       ZION   MKT   SMB   HML   RF       CPI   IndProd  \n",
       "Date                                                                         \n",
       "2013-01-31  11.253543   8.592044  5.57  0.39  0.95  0.0 -0.281588  0.405392  \n",
       "2013-02-28   0.481413   3.537714  1.29 -0.45  0.03  0.0 -0.209016 -0.172034  \n",
       "2013-03-31   0.615394   3.419136  4.03  0.78 -0.29  0.0  0.041407  0.109350  \n",
       "2013-04-30   1.621978  -1.491662  1.55 -2.42  0.63  0.0  0.237758  0.203532  \n",
       "2013-05-31   2.658907  13.259207  2.80  1.67  2.60  0.0  0.195554 -0.427080  \n",
       "\n",
       "[5 rows x 455 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv('../data/Data_Lecture_3.csv', index_col=0, parse_dates=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load CPI (costumer price index) \n",
    "# and IP (industrial production)\n",
    "cpi = df['CPI']\n",
    "ip = df['IndProd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IndProd</th>\n",
       "      <th>IndProd_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>0.405392</td>\n",
       "      <td>-0.172034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>-0.172034</td>\n",
       "      <td>0.109350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>0.109350</td>\n",
       "      <td>0.203532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>0.203532</td>\n",
       "      <td>-0.427080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>-0.427080</td>\n",
       "      <td>0.658091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             IndProd  IndProd_1\n",
       "Date                           \n",
       "2013-01-31  0.405392  -0.172034\n",
       "2013-02-28 -0.172034   0.109350\n",
       "2013-03-31  0.109350   0.203532\n",
       "2013-04-30  0.203532  -0.427080\n",
       "2013-05-31 -0.427080   0.658091"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the lagged variables (t-1) \n",
    "# for the regression model\n",
    "cpi_1 = cpi.shift(periods=-1)\n",
    "cpi_comb = pd.concat([cpi, cpi_1.rename('CPI_1')], axis=1)\n",
    "ip_1 = ip.shift(periods=-1)\n",
    "ip_comb = pd.concat([ip, ip_1.rename('IndProd_1')], axis=1)\n",
    "ip_comb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For CPI and IP, we run the following regressions:\n",
    "\n",
    "$CPI_t = \\beta_0 + \\beta_1 CPI_{t-1}$ \n",
    "\n",
    "$IP_t = \\beta_0 + \\beta_1 IP_{t-1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CPI regression\n",
    "model_cpi = sm.ols('CPI~CPI_1', data=cpi_comb).fit()\n",
    "beta_cpi_0 = model_cpi.params['Intercept']\n",
    "beta_cpi_1 = model_cpi.params['CPI_1']\n",
    "\n",
    "# IP regression\n",
    "model_ip = sm.ols('IndProd~IndProd_1', data=ip_comb).fit()\n",
    "beta_ip_0 = model_ip.params['Intercept']\n",
    "beta_ip_1 = model_ip.params['IndProd_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time series for CPI and IP:\n",
    "# The difference between the forecast and the actual return\n",
    "f_cpi = model_cpi.resid.rename('F_CPI')\n",
    "f_ip = model_ip.resid.rename('F_IP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAFBCAYAAAAsSvpRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3df7RkZXng++9j02j7IzYKKjS04h0uygRDmxOi03flGvwBapa0xBiYG4MZHdadpTeJueHaXGc0MXHZhkzMzIo3CaNGTIyiiEgGTIui4wSDoREUEAmITuxuIkRsjdKjDTz3j9oHqg9Vdarq7F373VXfz1pnnapdu/Z5ap9dz372u9/97shMJEmSJJXpEW0HIEmSJGk4C3ZJkiSpYBbskiRJUsEs2CVJkqSCWbBLkiRJBbNglyRJkgpWVMEeEe+NiLsi4qYhr0dE/OeIuD0ivhwRz+577eyIuK36OXt2UUuS+kXEMRHxmYi4JSJujohfGzDP0HwuSTpYUQU78D7gtBGvvxg4rvo5B/hjgIh4AvAW4KeBk4G3RMRhjUYqSRrmPuD/zsxnAs8BXhcRJ6yYZ2A+lyQ9XFEFe2Z+DrhnxCynA+/PnmuAjRFxJHAqcGVm3pOZ3wGuZHThL0lqSGbemZlfrB7/M3ALsGnFbMPyuSRphaIK9jFsAr7Z93x3NW3YdElSiyLiacAW4AsrXjJvS9KYDmk7gAnFgGk5YvrDFxBxDr3TrzzmMY/5yWc84xn1RSdJM3Ldddf9U2Ye0XYco0TEY4GPAr+emd9b+fKAtzwsb5uzJc2DtebsrhXsu4Fj+p4fDeytpj9vxfTPDlpAZl4AXACwtLSUu3btaiJOSWpURPyPtmMYJSLW0yvWP5CZlwyYZVg+P4g5W9I8WGvO7lqXmMuAX65GF3gO8N3MvBPYCbwoIg6rLjZ9UTVNkjRjERHAe4BbMvMPhsw2LJ9LklYoqoU9Ij5Ir6X88IjYTW/kl/UAmfknwBXAS4DbgXuBX6leuycifge4tlrUWzNz1MWrkqTmbAVeBdwYETdU0/5fYDOMzueSpIcrqmDPzLNWeT2B1w157b3Ae5uIS5I0vsz8Gwb3Ue+fZ2g+lyQdrGtdYiRJkqSFYsEuSZIkFcyCXZIkSSqYBbskSZJUMAt2SZIkqWAW7JIkSVLBLNglSZKkglmwS5IkSQWzYJckSZIKZsEuSZIkFcyCXZIkSSqYBbskSZJUMAt2SZIkqWAW7JIkSVLBLNglSZKkglmwS5IkSQWzYJckSZIKZsEuSZIkFcyCXZIkSSqYBbskSZJUMAt2SZIkqWAW7JIkSVLBLNglSZKkglmwS5IkSQWzYJckSZIKVlTBHhGnRcStEXF7RGwf8Po7I+KG6ufvI2Jf32v397122WwjlyQti4j3RsRdEXHTkNefFxHf7cvZb551jJLUJYe0HcCyiFgHvAt4IbAbuDYiLsvMryzPk5lv6Jv//wK29C1if2aeNKt4JUlDvQ/4I+D9I+b575n5c7MJR5K6raQW9pOB2zPzjsz8EfAh4PQR858FfHAmkUmSxpaZnwPuaTsOSZoXJRXsm4Bv9j3fXU17mIh4KnAscFXf5EdFxK6IuCYitjUXpiSpBs+NiC9FxCci4l+2HYwklayYLjFADJiWQ+Y9E7g4M+/vm7Y5M/dGxNOBqyLixsz82sP+SMQ5wDkAmzdvXmvMkqTJfRF4amZ+PyJeAlwKHDdoRnO2JJXVwr4bOKbv+dHA3iHznsmK7jCZubf6fQfwWQ7u394/3wWZuZSZS0ccccRaY5YkTSgzv5eZ368eXwGsj4jDh8xrzpa08Eoq2K8FjouIYyPiUHpF+cNGe4mI44HDgL/tm3ZYRDyyenw4sBX4ysr3SpLaFxFPiYioHp9Mb1/07XajkqRyFdMlJjPvi4jXAzuBdcB7M/PmiHgrsCszl4v3s4APZWZ/d5lnAn8aEQ/QS/w7+keXkSTNTkR8EHgecHhE7AbeAqwHyMw/AV4B/LuIuA/YD5y5IqdLkvoUU7DDg6dGr1gx7c0rnv/WgPd9Hjix0eAkSWPJzLNWef2P6A37KEkaQ1EFuyRJkqZ36fV7OH/nrezdt5+jNm7g3FOPZ9uWgYPuqUMs2CVJkubApdfv4bxLbmT/gd4genv27ee8S24EsGjvuJIuOpUkSdKUzt9564PF+rL9B+7n/J23thSR6mLBLkmSNAf27ts/0XR1hwW7JEnSHDhq44aJpqs7LNglSZLmwLmnHs+G9esOmrZh/TrOPfX4liJSXbzoVJIkaQ4sX1jqKDHzx4JdkiRpTmzbsskCfQ7ZJUaSJEkqmAW7JEmSVDALdkmSJKlgFuySJElSwSzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2SJEkqmAW7JEmSVDALdkmSJKlgFuySJElSwSzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2SJEkqWFEFe0ScFhG3RsTtEbF9wOuvjoi7I+KG6ue1fa+dHRG3VT9nzzZySdKyiHhvRNwVETcNeT0i4j9Xuf7LEfHsWccoSV1STMEeEeuAdwEvBk4AzoqIEwbMelFmnlT9vLt67xOAtwA/DZwMvCUiDptR6JKkg70POG3E6y8Gjqt+zgH+eAYxSVJnFVOw0yu0b8/MOzLzR8CHgNPHfO+pwJWZeU9mfge4ktE7C0lSQzLzc8A9I2Y5HXh/9lwDbIyII2cTnSR1T0kF+ybgm33Pd1fTVvr56hTqxRFxzITvlSS1z5wtSRMoqWCPAdNyxfO/Ap6Wmc8CPgVcOMF7ezNGnBMRuyJi19133z11sJKkqZmzJWkCJRXsu4Fj+p4fDeztnyEzv52ZP6ye/hfgJ8d9b98yLsjMpcxcOuKII2oJXJI0EXO2JE2gpIL9WuC4iDg2Ig4FzgQu659hRR/HlwG3VI93Ai+KiMOqi01fVE2TJJXnMuCXq9FingN8NzPvbDsoSSrVIW0HsCwz74uI19MrtNcB783MmyPircCuzLwM+NWIeBlwH70Lml5dvfeeiPgdekU/wFszc9QFT5KkhkTEB4HnAYdHxG56o3itB8jMPwGuAF4C3A7cC/xKO5FKUjdE5sBugwthaWkpd+3a1XYYkjSxiLguM5fajmOWzNmSumqtObukLjGSJEmSViimS4wkSYvm0uv3cP7OW9m7bz9HbdzAuacez7YtjnAp6WAW7JIkteDS6/dw3iU3sv/A/QDs2bef8y65EcCiXUXxwLJ9domRJKkF5++89cFifdn+A/dz/s5bW4pIerjlA8s9+/aTPHRgeen1e9oObaFYsEuS1IK9+/ZPNF1qgweWZbBglySpBUdt3DDRdKkNHliWwYJdkqQWnHvq8WxYv+6gaRvWr+PcU49vKSLN2qXX72Hrjqs4dvvlbN1xVZHdTDywLIMFuyRJLdi2ZRNvP+NENm3cQACbNm7g7Wec6MV8I3ShwB1XV/qGe2BZBkeJkSSpJdu2bLJAH1PJo+pMM4rKqL7hbX+efsuxOEpMuyzYJUlS8UotcKc9kOhS33APLNsf2tIuMZIkqXilFrjTjqJi3/AyjNPNqoTuSxbskg4yT31EJc2PUgvcaQ8k7BvevnEL8RKGtrRgl/SgEloRJGmQUgvcaQ8k5v2i4y40/oxbiJdwdsc+7JIeVGofUWmetNkXtu1+uGtR6sWP5556/EF92GH8A4l57Rte8gXC/cYtxI/auIE9A+ad5dkdC3ZJDyqhFUGaZ20WMl0pokYpscAt9UCiTV1p/Bm3EF/LQVldLNglPaiEVgRpnrVZyHSliOqiEg8k2tSVxp9xC/ESDsos2CU9qIRWBGmetVnIdKWIUvd1pfFnkkK87YMyC3apA2bV77SEVgRpnrVZyHSliFJ3DNs3danxp+1CfFwW7FLhZt3vtCvJS+qiNguZLhVRKt84+yYbf+pjwS4Vzn6n0vxos5CxiFKdVts32fhTLwt2qXD2O5XmS5uFjEWU6uK+aba8cZJUuFLv7idJWlzum2bLgl0qXKl395MkLS73TbNllxipcPY7ldrT5TuDSk1y3zRbFuxSB9jvVJq9ebgzqMozTweB7ptmp6guMRFxWkTcGhG3R8T2Aa//RkR8JSK+HBGfjoin9r12f0TcUP1cNtvIJUn9xsjnr46Iu/vy9mvbiHOUUaNgSNNYPgjcs28/yUMHgZdev6ft0FS4YlrYI2Id8C7ghcBu4NqIuCwzv9I32/XAUmbeGxH/Dvg94Ber1/Zn5kkzDVqS9DBj5nOAizLz9TMPcEyOgrE289SSXBeH6dW0iinYgZOB2zPzDoCI+BBwOvBggs/Mz/TNfw3wSzONUNJQbe6cF/VvF2zVfN4F3hl0enYnGsyDQE2rpC4xm4Bv9j3fXU0b5jXAJ/qePyoidkXENRGxrYkAJQ3W5mneRf3bhRs3n/981cXx4og4Zjahjc9RMKZnd6LBHApR0yqpYI8B03LgjBG/BCwB5/dN3pyZS8C/Bv4wIv6XIe89pyrsd919991rjVlTuPT6PWzdcRXHbr+crTuu6nxxM2+fZxpt7pwX9W8Xbpx8/lfA0zLzWcCngAsHLqjFnL1tyybefsaJbNq4gQA2bdzA2884caFbiMdlS/JgHgRqWiV1idkN9LewHA3sXTlTRLwAeBPwv2fmD5enZ+be6vcdEfFZYAvwtZXvz8wLgAsAlpaWBh4QqDnzdpp03j7PtNrcOS/q3y7cqvk8M7/d9/S/AO8YtKC2c7ajYEzH7kSDORTiYHYtXF1JBfu1wHERcSywBziTXmv5gyJiC/CnwGmZeVff9MOAezPzhxFxOLCV3gWpKsy8XXAzb59nWm3unJv62+PsQCxKhhonnx+ZmXdWT18G3DLbECdnUTG+c089/qDGDLAledksDwKn3WZnua3b8DWeYrrEZOZ9wOuBnfQS94cz8+aIeGtEvKya7XzgscBHVgzf+ExgV0R8CfgMsGPAaAQqwLy1SM7b55lWm6d56/zby92bnrb9ct5w0Q2r9k339PZgY+bzX42Im6u8/avAq9uJdjxerzAZuxO1b9ptdtbbul0Lx1NSCzuZeQVwxYppb+57/IIh7/s8cGKz0akO89YiOW+fZ1ptnuat62+vbOVZ2fdi0JkTT28PN0Y+Pw84b9ZxTcuzaZOzO1G7pt1mZ72t2/A1nqIKds2/eTtNOm+fZy3a3DnX8bcH7aRWGrQDsShZDBYV6pppt9lZb+s2fI2nmC4xWgzzdpp03j7PIhtnZ+QOZHE5HJ+6Ztptdtbbul0Lx2MLu2Zu3lok5+3zLKphrTzL6tiBeNFid3k2TV0z7TY7623droXjsWCXJAbvpIJeX/ZNNexAHAmh2ywq5seiHDhPu822sa3b8LW6yFzcociXlpZy165dbYehERYlsaoMTW5vW3dcNbAFf9PGDVy9/ZSJlxcR11U3i1sYk+Zs84dWWnngDL3WY7syqmlrzdm2sM+YO5DR+tfP4zes5wc/uo8D9/cOKm2RVNOabOXxosXZ8oyGBnG0n2ZZ4zTHi05nyHF8R1u5fvbtP/Bgsb6srbFZl8fnPnb75WzdcZX/M03MixZny7GdNYgHzs2xxmmWBfsMuQMZbZxh9WD2ibWpJORBwGJxJITZsjDTIB44N8cap1kW7DPkDmS0cdfDrBNrE0moqy0RHmRMzyFAZ2veCjO/e/XwwLk51jjNsg/7DNV1c4B57SO22rB60E5ibSIJdbEfZVN9gud1ex7EkRBmZx6GYVz+buzZt//BEYvA/vhrseij/TSZb70BUrMs2Geojh3IPF9INWj9rH9E8NhHHcK+ew+0llibSEJdbIlo4iBjnrdntavtwmythdHK78bK8dxKP8Av2aIeOK+Wb9e6zc7DQXLJLNhnqI4dSBdbZsfV9g52mCaSUGktEeMkas80qGvaKszqOBAd55qekg/wVZ7VuneudZstdR8+LyzYZ2ytO5AutsxOosSWjyaSUEktEeMWF5MeZLR1ECC1rY4D0XG+A2v57mnxjMq3dTWelLgPnxcW7B2xnICH3ebKPmLNqjsJldQSMW6inuQgo6mDgHliUTVf+v+fw/L0JAeiq13Ts9bvnhbPqHxr40n5HCWmAXVfzd8/osggi9JHbN5GSdi2ZRNXbz+Fr+94KVdvP6W1nem4iXqSUU7GHVlnUUds6OooQRps5f9zmEkORAd9N6L6Xcd3T4tnVL6dt1GV5pEt7DVronVjVF/GTQvSMmerUXMmaeUe90zDJAcBUMaZhlmy7/58Gae/+aQHotN+N2wpndyinO1abZsqpZumBrNgr1kTO+JhiTaAq7efMtUyu6a0AmeeEnzbF9UuYp9Hi6r5Mur/FjB1jpjmu7HI3cymsWiNQcO2qUVtPOkSC/aaNbEjdvz2ta3Xuj/3LBL8tDFP876SL6rt8jY7ikVVWda6nQ37f27auGHmjSolXdDeBaU1BrVpERtPusSCvWZN7Ignvdhv0I6n660I067XWXVRqjPBTxvzWj5riRfVdn2bHcWiqhx1bGdt/D+H5XpbSiczSWPQvDYgzKt5+39ZsNesicQ9bgIeteNZrcgsfcOedr3OsotSXd0Zpo25tJaitR4ElPZ56mRRVY46trNZ/z9XO8iwpXR84zYGzXMDQhuarjnm8f9lwV6zphL3OAl41I5nVJHZhQ27pAuwmu7OMG3M89Avuu6h8UpmUVWGur43s/x/zvPB7KyN2xjkOq/PLGqOefx/WbA3oK0d8bAdzJ59+1kXwf358BLoqI0bOrNhl3IBVh1dlNYS87Bldr1f9MokPkxXPo+6oYvfm3k4OC/FuI1Bbazz0s98T2sWNcc8fkcmLtgj4hJgN3Ad8EXg5sx8oO7A5sUsv3CjbrQxqFhfLjLfcNENA9/T5Q172Vq6KK21j+i0rQijYh61zK73i25iaDxpNV383nTxIKNk4zQGzXqdd+HM97TqLKbntQFrkGlunHQ28PvAPuAXgA/XGtEcmfXNUQbdFGGldREPu+nNPN8wYZKb/fRb7X83zk2Ppr2ByaiYV2uZmOazlmK1ofE2bljPo9Y/gjdcdMNc3DhLZeji92ZRbzjWplmv83m+AVZdNceo/fQ8fkem6RLzMeDrwOcz8801xzNXZt3VpL/ld1hL+wOZfH3HSx88Kn3DRTfw+A3rWb8uOHD/Q63wdW/YbZ7am6YrTR3/u7W0IgyLebVldrlf9Kih8Va2gs5Ta1NbIuKxwGGZ+c22Y2lb1743XrQ8e5Ou87UOzTtsH97lM9/9ny3goOuUpqk5Ru2nl4dTnafvyDQF+62Z+bqI+I91BxMRpwH/CVgHvDszd6x4/ZHA+4GfBL4N/GJmfqN67TzgNcD9wK9m5s6645tUG2OHL+94tu64aujpoJWn2vbtP8D6RwSHPXo9++49UPuG3dSpvVHraK0HCHWcshtWgD4igmO3X17reM/zcDZkVNeErlxn0TEfB+6IiM9n5p/VvfC15HOtrmsHGW2ou6Fo3HVe19C8g3Q116/8bAkPFu3T3rF9nhuwBpmmS8xPRcT5wP8aEcdFRNQRSESsA94FvBg4ATgrIk5YMdtrgO9k5r8A3gm8o3rvCcCZwL8ETgP+v2p5rZr2tE8dXWlGnQ4aVPwceCB59KGHjOziMa0mTu2NWkeTrL9Lr9/D1h1Xcez2yw/qZlHHKbthXZTuz2zk/9oVw9b5qK4J83gBUQG+mpn/Fvjxuhe8lnw+C8O2Qc2PWXdJ7TftPm+163i6luv7Dfpsy8X6tDXHPHfnHWTigj0zT6bXavJu4FXU14f9ZOD2zLwjM38EfAg4fcU8pwMXVo8vBp5fHTCcDnwoM3+YmV8Hbq+W16ppi6s6CtySip8m/t6odTTu+mu6/9vK/8G6Ace2df5f2zBp4TPttQGLlphnpJHGl8pa8nmj2izkNDtt9gGve2heaD/Xr1UTdcA8NGBNYppRYt4O/IfM/HhE/BXwuJpi2QT096XcDfz0sHky876I+C7wxGr6NSve2/pW3fbY4cNOB62lW0UTwxROY5p1tPK1WfR/6/8fHLv98oljXm2Zk6j79PA0p32n7drS9kge8zi8WmaeHBFH0+uS8irgmfQGEqjDWvL5P9UUw0B2r1oMbZ6Vm3afN+o6nuV9UldNsk7GzbeT1FjzkMOn6cP+2My8DyAzH4iIP6B3anOtBrWsrByLcNg847y3t4CIc4BzADZv3jxJfFMpZezwftMWP00MUzit1dbROOtv1v3f2ux/3sR1BNMUPtPuRNu8yG7SddeVHcOKIXo/Atxc5+IHTBs3nx88U8052+5Vi6HNfDvtPq/thokmjfvZJs234+yn52WIzGn6sK8cc/37dQRCb8dxTN/zo4G9w+aJiEOAxwP3jPleADLzgsxcysylI444oqbQ69X0aZ5pu1U0MUzhtEato3HX36y7WbR5+q6J08PTFD5rWefjDKXZhEnWXcndLSLiwv7nmXkG8Hs0M0TvWvL5QerO2SV3r7JvfX3azLfT7vNK6/JYp3E/WxP7qnkZInOaFvarI+L3gT+kNyJLXVXvtcBxEXEssIfeRaT/esU8l9EbB/5vgVcAV2VmRsRlwF9Wrf1HAccBf1dTXDM3i9bEaVqPmximcFrjrKPV1t+sWzPabCVuolVxmhasLrYgTbLuCu9u8azlBxHxycx8UWbuplc4f7zmvzV1Pq85jocpdRucl1bAUrQ99OW0+7x5G9mk3zifrYl91bycVVu1YI+ICzPz7OXnmfnhiPgH4N8DjwR+t45Aqj6Mrwd20hsG7L2ZeXNEvBXYlZmXAe8B/jwibqfXEnNm9d6bI+LDwFeA+4DXZeboWyYWbpLho9q+k2pbLVOj1tE466+NhN5WMm7ifzdN4dP2TnQak6y7wncM/cVwo6cX15LPm1bqNlj4wV4n1ZFvu9LFbV40sa8qrXaZ1jgt7INaZa7h4Is8a5GZVwBXrJj25r7H/5MhF0Vl5tuAt9UdU5PWmghm1SJT980OSjLPrRn9mmhVnLbw6do6n2TdFb5jeEpEvBr4EoP7j9dqLfm8aSVug4Uf7C2kks56LMqBQxP7qlLPqk1qnIJ9Zq0yi6SORDCLFpkmbnagwZpMyE21KpZY+NRtknVX+I7ht4Al4FeAoyPiRnoXmt4MfCUzP9pibAuvqYO9RSn0mlDKWY+SDhya1sS+qtSzapMap2CfaavMoqgjEcyiRWa1mx2oHrNIyItQXE9qkuHDxll3Je8YMvOC/ufVkI7PAk4EtgEW7C1q4mBvkQq9JpRy1qOUA4dZaWJfNQ/7v3EK9t/CVpna1ZEIZnH6vZSENe8WLSHP0rCivKlipis7hr4LTq9YbV41r4mDPfPK2pTSxc39sGCMgt1WmWbUkQhmcfq9lIQ170zI9Rp23UV/UW4xo9LUfbBnXlmbUrq4uR8WTDGso60y9agjETR1+r2/RfLxG9azfl1w4P6HLmUoqE/u3DAh12fQdRf9lotyixnNO/PK5Faekfv5n9zEZ756d6td3FarF7xOYTFMMw67ajCo2P7ZZxzB+Ttv5Q0X3dDaiBsri519+w+w/hHBYY9ez757D5gMGlJKS05TZrlDGdRyvtJyHBYzmmfznlfqNqib3Eev29P6zYtGNc55ncLisGBvUX+xXcqXblCxc+CB5NGHHsL1b37RzOJYNCVfrLhWs962x2khX16/FjOaZ6vllUVqmR3ns5bcTW5Y41zJMateFuyFKOVLt5ZuAouU/JvQlYsVJzXrbXtYy/my5aJ8ng+StFhG5d5heaWNRqK29hHjftYudpPrYsyajgV7IUr50k3bTaCUMwQqz6y37UEt58PuHTCvB0laHNPm3lkfSLe5jxj3s3axm1wXY9Z0HtF2APPg0uv3sHXHVRy7/XK27riKS6/fM/Eyhn25Zv2lO/fU49mwft1B08bpJjAqIarb1rp9z3rb3rZlE28/40Q2bdxA0CvS3/mLJ/GNHS/l6u2nWKBrrkybe2d9IN3mPmLczzrt/q9NXYxZ07GFfY3qajUopT/tJN0E+k9vrhyJY5mn5bqtju27jW3blnOtNK9d9qYtvGfdMtvmWeRxP2sXu8l1MWZNx4J9jeo6rVjSl26cYmdlITeMp+XKNG7xUsf2XdK2rcXUVHeMEg4Cpi28Z30g3WbXjUk+axcP9rsYsyZnwb5GdbYadOlLN87QeeMm/xJ2evOojjt81rV9N7Ftt7nduM12SxP9tUu5bmfawnvWB9JtnkW20UDzwIJ9jRb1go9RBVvA2AlxHkcqKKGYG7VeJyleSt2+2yyWSinUNL4mumOUMrLXWorRWTYStV00d6lBTM0rYT89KQv2NSql7/msDSvkNm3cwNXbTxl7OfM2UkEpxdyo9TpJ8VLq9r2W7WatibqUQk3ja+LAs5SRvaA7xWhX4tR8K2U/PSlHiVmjQSNStH1XtFmo68r0eRupoJTRckat10lGbSl1+552u1lO1HuqC6WXE/UkI9+UVKhpPE2MpFHKyF6zUsdoaPPGddJNpeynJ2ULew0WsdWgrtOb8zZSQSnF3Kj1OmmreYnb97TbTR2t46V2E9JwTXTHKPXsUxO62iLZJNdJd5Wyn56UBbumVkchN28jFZRSzI1ar233Ja3DtNtNHYl6kQq1eVL3gec8fI/GZTewh5uHddLFftx1KGU/PSkLdrVq3kYqKKWYW229lthqPolpt5s6EvUiFWoarevfo3F1tUWySV1fJ4t8hqCU/fSkLNjVunkaqaCkYm7ei4lpPl9diXrcv72oLViaL11tkWxS19fJPJwhmFZJ++lJWLBr4TRdyM66ULYoHN8sE/Uit2BpvsyiRbJreayrrbTLun6GYK262KBlwS51mEXh5GaVqBe5BUvzpekD3S7msa620i7r+hmCRWTBLnWYRWG5Fr0FS/Ol7gPd/hb1R0Rwf+ZBr3chj3WxlXZZ188QLCILdqnDLArLZQuWNNjKFvWVxfoy81hzunSGoGvdpZpiwS51mEVhuWzB0iIap7gadGZwEPNYs7pwhqCL3aWaUsSdTiPiCRFxZUTcVv0+bMA8J0XE30bEzRHx5Yj4xb7X3hcRX4+IG6qfk2b7CaR2NNWTrlcAABXZSURBVHEHR9Wj1LvENm2cfF7Nd39fzr5s1nGqfuPeSXiclnPzmKC7dyVtQikt7NuBT2fmjojYXj1/44p57gV+OTNvi4ijgOsiYmdm7qtePzczL55hzFLrunRacxF1oQWrAePkc4D9mWnjSsdN0xd92JnBdRE8kGke04Ps9vmQUgr204HnVY8vBD7LigSfmX/f93hvRNwFHAHsQ1pgC1oUqlyr5nPNh2n7og/rLjavZ6Dsgz09u30+pIguMcCTM/NOgOr3k0bNHBEnA4cCX+ub/Laqq8w7I+KRdQd46fV72LrjKo7dfjlbd1z1sFN8kiRg/Hz+qIjYFRHXRMS22YWnukzbF32RuouN201Ig82622fJtd7MWtgj4lPAUwa89KYJl3Mk8OfA2Zn5QDX5POAf6RXxF9BrzXnrkPefA5wDsHnz5rH+phc9SNJDasrnm6uzpU8HroqIGzPzaytnmiZnazbW0hd9Uc4MOvTu2nizu4fMrGDPzBcMey0ivhURR2bmnVVBfteQ+X4MuBz495l5Td+y76we/jAi/gz4zRFxXECvqGdpaWnw+bsV/MJJ0kPqyOeZubf6fUdEfBbYwsFnTZfnmzhnr0UT3Re60iVi0jjti766pvpgd2WbqoM3u+sppUvMZcDZ1eOzgY+vnCEiDgU+Brw/Mz+y4rUjq98BbANuqjM4L3qQpLGNk88PW+66GBGHA1uBr8wswiGa6L7QlS4R08Q5rLvCf3zlT/D1HS/l6u2nFFHotGlYX+u19MHuyjbVNaXXeqUU7DuAF0bEbcALq+dExFJEvLua55XAzwCvHjB84wci4kbgRuBw4HfrDG7YF+sREUX2c5KkFo2Tz58J7IqILwGfAXZkZusFexNDyHVlWLphcf76RTcM3cctUl/0aTXRB7sr21TXNHFwVaciRonJzG8Dzx8wfRfw2urxXwB/MeT9pzQZ36Ar2uGhK+JL6+ckSW0ZM59/HjhxxqGtqokWttJb7ZaNimfUPm5R+qJPq4k+2F3ZpprQZFeg0m92V0TBXrqVX7hxx5qVJHVHE0PIdWVYumFxLnMfN726D2q6sk3VremLQku/r0kpXWKKt23LJq7efgpf3/FSHhhzrFlJUnc00X2hK3cjHhTnSu7jytCVbapus+gK1F/rlXYNhi3sU1jUo9t5t0hX3Ut6uCZa2EpvtVvWH+ewlnb3cWXoyjZVt0XuCgQW7FMpvZ+TJlf6+KuSZqOJPtld6ee9HOfKfAgH7+Ns3GhfV7apOi16Y6kF+xQW9ei2ZGvdgZQ+/qokzcqofZyNG2pLG42lJR2cWrBPaRGPbktVxw5k0U+1SVK/Yfs4GzfUllk3lpZ2cGrBrs6rYwey6KfaJGkcbTRulNTKqXbNsrG0tINTR4lR59WxA1nUq+4laRKzvrmMd/VUW0o7827Brs6rYwfiHfskaXWzbtzwrp4a16XX72HrjqtquwN9aXc+tUuMOq+uC1G8LkGSRptFP+L+LjCD73ryUCun3WUEzfQ3L21EQAt2dZ6j9kjS7DTZuDFoSMlBjtq4obiLAtWeJvqbl1ZbWLBrLtg6LkndN6jwWmm5lbO0iwLVnqb6m5dUW9iHXZIkFWFUgbXy+qLSLgpUe0rrb94EW9glSVIRhg2xu2njBq7efspY885TkabxlNbfvAm2sEuSpCJMMgqNw/Fq2SKM9GYLuyRJKsIkF/qVdlGg2lVSf/MmWLBLkqRiTFJ4zXuRJi2zS4wkSZJUMFvYJUlS53kTJc0zC3ZJktRp3kRJ884uMZIkqdNG3URJmge2sEuSpE7zJkqzY9ejdtjCLkmSOm0R7nRZguWuR3v27Sd5qOvRpdfvaTu0uWfBLkmSOs2bKM2GXY/aY5cYSZLUad5EaTbsetSeIgr2iHgCcBHwNOAbwCsz8zsD5rsfuLF6+g+Z+bJq+rHAh4AnAF8EXpWZP2o+cklSv4j4BeC3gGcCJ2fmriHznQb8J2Ad8O7M3DGzIDWXvIlS847auIE9A4pzux41r5QuMduBT2fmccCnq+eD7M/Mk6qfl/VNfwfwzur93wFe02y4kqQhbgLOAD43bIaIWAe8C3gxcAJwVkScMJvwJE3LrkftKaVgPx24sHp8IbBt3DdGRACnABdP835JUn0y85bMXK1D68nA7Zl5R3U29EP09gOSCrZtyybefsaJbNq4gQA2bdzA28840TMbM1BElxjgyZl5J0Bm3hkRTxoy36MiYhdwH7AjMy8Fngjsy8z7qnl2A245klSuTcA3+57vBn66pVgkTcCuR+2YWcEeEZ8CnjLgpTdNsJjNmbk3Ip4OXBURNwLfGzBfjojjHOAcgM2bN0/wpyVJMDqfZ+bHx1nEgGkD87Y5W5JmWLBn5guGvRYR34qII6vW9SOBu4YsY2/1+46I+CywBfgosDEiDqla2Y8G9o6I4wLgAoClpaWhhb0kabBR+XxMu4Fj+p4PzdvmbEkqpw/7ZcDZ1eOzgYe10ETEYRHxyOrx4cBW4CuZmcBngFeMer8kqRjXAsdFxLERcShwJr39gCRpgFIK9h3ACyPiNuCF1XMiYiki3l3N80xgV0R8iV6BviMzv1K99kbgNyLidnp92t8z0+glSQBExMsjYjfwXODyiNhZTT8qIq4AqM6Gvh7YCdwCfDgzb24rZkkqXfQaqBfT0tJS7to1cIhgSSpaRFyXmUttxzFL5mxJXbXWnF1KC7skSZKkASzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2SJEkqmAW7JEmSVDALdkmSJKlgFuySJElSwSzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2SJEkqmAW7JEmSVDALdkmSJKlgFuySJElSwSzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2SJEkqmAW7JEmSVDALdkmSJKlgFuySJElSwYoo2CPiCRFxZUTcVv0+bMA8PxsRN/T9/M+I2Fa99r6I+HrfayfN/lNIkiLiFyLi5oh4ICKWRsz3jYi4scrZu2YZoyR1TREFO7Ad+HRmHgd8unp+kMz8TGaelJknAacA9wKf7Jvl3OXXM/OGmUQtSVrpJuAM4HNjzPuzVc4eWthLksop2E8HLqweXwhsW2X+VwCfyMx7G41KkjSRzLwlM29tOw5JmielFOxPzsw7AarfT1pl/jOBD66Y9raI+HJEvDMiHtlEkJKk2iTwyYi4LiLOaTsYSSrZIbP6QxHxKeApA15604TLORI4EdjZN/k84B+BQ4ELgDcCbx3y/nOAcwA2b948yZ+WJDE6n2fmx8dczNbM3BsRTwKujIivZubDutGYsyVphgV7Zr5g2GsR8a2IODIz76wK8rtGLOqVwMcy80Dfsu+sHv4wIv4M+M0RcVxAr6hnaWkpJ/kMkqTR+XyCZeytft8VER8DTmZAv3dztiSV0yXmMuDs6vHZwKgWmrNY0R2mKvKJiKDX//2mBmKUJNUgIh4TEY9bfgy8CPO2JA1VSsG+A3hhRNwGvLB6TkQsRcS7l2eKiKcBxwD/bcX7PxARNwI3AocDvzuDmCVJK0TEyyNiN/Bc4PKI2FlNPyoirqhmezLwNxHxJeDvgMsz86/biViSyjezLjGjZOa3gecPmL4LeG3f828AmwbMd0qT8UmSxpOZHwM+NmD6XuAl1eM7gJ+YcWiS1FmltLBLkiRJGqCIFnbNn0uv38P5O29l7779HLVxA+eeejzbtjzs5IgkSZJWYcGu2l16/R7Ou+RG9h+4H4A9+/Zz3iU3Ali0S5IkTcguMard+TtvfbBYX7b/wP2cv9ObH0qSJE3Kgl2127tv/0TTJUmSNJwFu2p31MYNE02XJEnScBbsqt25px7PhvXrDpq2Yf06zj31+JYikiRJ6i4vOlXtli8sdZQYSZKktbNgVyO2bdlkgS5JklQDu8RIkiRJBbNglyRJkgpmwS5JkiQVzIJdkiRJKpgFuyRJklQwC3ZJkiSpYBbskiRJUsEs2CVJkqSCWbBLkiRJBbNglyRJkgpmwS5JkiQVzIJdkiRJKpgFuyRJklQwC3ZJkiSpYBbskiRJUsEs2CVJkqSCFVGwR8QvRMTNEfFARCyNmO+0iLg1Im6PiO1904+NiC9ExG0RcVFEHDqbyCVJ/SLi/Ij4akR8OSI+FhEbh8w3MJ9Lkh6uiIIduAk4A/jcsBkiYh3wLuDFwAnAWRFxQvXyO4B3ZuZxwHeA1zQbriRpiCuBH8/MZwF/D5y3coZV8rkkaYUiCvbMvCUzb11ltpOB2zPzjsz8EfAh4PSICOAU4OJqvguBbc1FK0kaJjM/mZn3VU+vAY4eMNvAfD6rGCWpa4oo2Me0Cfhm3/Pd1bQnAvv6dhDL0yVJ7fo3wCcGTB+WzyVJAxwyqz8UEZ8CnjLgpTdl5sfHWcSAaTli+rA4zgHOqZ5+PyJWa9kHOBz4pzHma1sX4uxCjNCNOLsQI3Qjzi7ECAfH+dS2ghgnn0fEm4D7gA8MWsSAaQPztjm7dV2IEboRZxdihG7E2YUYocacPbOCPTNfsMZF7AaO6Xt+NLCX3orYGBGHVK3sy9OHxXEBcMEkfzgidmXm0IthS9GFOLsQI3Qjzi7ECN2IswsxQjlxrpbPI+Js4OeA52fmoEJ8WD4f9LfM2S3qQozQjTi7ECN0I84uxAj1xtmlLjHXAsdVI8IcCpwJXFbtDD4DvKKa72xgnBZ7SVLNIuI04I3AyzLz3iGzDczns4pRkrqmiII9Il4eEbuB5wKXR8TOavpREXEFQNV6/npgJ3AL8OHMvLlaxBuB34iI2+n1aX/PrD+DJAmAPwIeB1wZETdExJ/ARPlckrTCzLrEjJKZHwM+NmD6XuAlfc+vAK4YMN8d9EYdaMpEp2Nb1IU4uxAjdCPOLsQI3YizCzFCB+LMzH8xZPpY+bwmxa+nShfi7EKM0I04uxAjdCPOLsQINcYZg7sXSpIkSSpBEV1iJEmSJA22kAV7RBwTEZ+JiFsi4uaI+LVq+hMi4sqIuK36fVg1/RkR8bcR8cOI+M2+5TwqIv4uIr5ULee3S4yzb3nrIuL6iPivJcYYEd+IiBurfq+76oqxgTg3RsTF1e3Xb4mI55YUY0QcX63D5Z/vRcSv1xFjnXFWr72hWsZNEfHBiHhUgTH+WhXfzXWuxynj/D8i4svVz+cj4if6lnVaRNwaEbdHxPY642xbjd8Nc7Y525xdYM5uIM5G8vYUMdaXszNz4X6AI4FnV48fR+/22ScAvwdsr6ZvB95RPX4S8FPA24Df7FtOAI+tHq8HvgA8p7Q4+5b3G8BfAv+1xBiBbwCHl/w/r167EHht9fhQYGNpMfYtcx3wj8BTS1uX9G6U83VgQ/X8w8CrC4vxx4GbgEfTu+bnU8BxLa7LfwUcVj1+MfCFvv/z14CnV9vkl4ATmvgutfFT4//TnG3ONmcXmLNrjrOxvD1FjLXl7IVsYc/MOzPzi9Xjf6Y3SsEmerfGvrCa7UJgWzXPXZl5LXBgxXIyM79fPV1f/dR2UUBdcQJExNHAS4F31xVf3TE2qa44I+LHgJ+hGokoM3+UmftKinGF5wNfy8z/UUeMDcR5CLAhIg6hl1yH3kOhpRifCVyTmfdmb2ST/wa8vI4Yp4zz85n5nWr6NfTGL4feRfe3Z+Ydmfkj4EPVMuaCObs+5mxz9hrjbCRn1xxnY3m7zZy9kAV7v4h4GrCFXkvLkzPzTuj9U+gdva32/nURcQNwF3BlZn6hxDiBPwT+H+CBJuKDWmJM4JMRcV307m5YYpxPB+4G/ix6p6rfHRGPKSzGfmcCH6w7vmVriTMz9wC/D/wDcCfw3cz8ZEkx0mul+ZmIeGJEPJreKCfHrPKeWcX5GuAT1eNNwDf7XttdTZs75uz6mLOLibHfwufstcbJjPL2rHP2QhfsEfFY4KPAr2fm96ZZRmben5kn0TtqOjkifrzOGGHtcUbEzwF3ZeZ1dcfW9zfWvC6BrZn5bHqnjV4XET9TW4CVGuI8BHg28MeZuQX4Ab3TX7WpaV0SvRvSvAz4SF2xrVj+WrfLw+i1KBwLHAU8JiJ+qaQYM/MW4B3AlcBf0ztteV+dMcLkcUbEz9JL/m9cnjRgtrkbAsycXR9zdn3M2fXqQt5uI2cvbMEeEevprewPZOYl1eRvRcSR1etH0muBGUt1iu2zwGkFxrkVeFlEfIPeaZdTIuIvCouR7I3TTGbeRW9c/lrH1q8pzt3A7r5WuYvp7QxKinHZi4EvZua36oqv5jhfAHw9M+/OzAPAJfT6+5UUI5n5nsx8dmb+DHAPcFtdMU4TZ0Q8i143idMz89vV5N0c3IJ0NDWeqi6BOducPWWc5uz64mw0Z9cYZ6N5u62cvZAFe0QEvf5st2TmH/S9dBlwdvX4bODjqyzniIjYWD3eQG9j/mppcWbmeZl5dGY+jd7ptqsys5aj4hrX5WMi4nHLj4EX0TutVYsa1+U/At+MiOOrSc8HvlJSjH3OooFTqzXG+Q/AcyLi0dUyn0+vP2BJMRIRT6p+bwbOoMZ1OmmcVQyXAK/KzL/vm/9a4LiIOLZqpTuzWsZcMGebs6eN05zdjZxdc5yN5e1Wc3bWfGV3F36A/43eqYcvAzdUPy8Bngh8mt6R2KeBJ1TzP4Xe0dD3gH3V4x8DngVcXy3nJuDNJca5YpnPo94RB+pal0+nd9rqS8DNwJtKXZfAScCualmXUl0BXliMjwa+DTy+1O9P9dpv0yuYbgL+HHhkgTH+d3o7+C8Bz295Xb4b+E7fvLv6lvUSeiMWfK3u70/bP3X9PzFn17kuzdnm7NpydgNxNpK3p4ixtpztnU4lSZKkgi1klxhJkiSpKyzYJUmSpIJZsEuSJEkFs2CXJEmSCmbBLkmSJBXMgl2aQPT8TUS8uG/aKyPir9uMS5L0cOZszQuHdZQmFL1bmX8E2AKsoze26mmZ+bU1LPOQzKz9lveStOjM2ZoHFuzSFCLi94AfAI8B/jkzfycizgZeBxwKfB54fWY+EBEX0LsV9gbgosx8a7WM3cCf0rs1+h9m5kda+CiSNPfM2eq6Q9oOQOqo3wa+CPwIWKpacF4O/KvMvK9K+GcCfwlsz8x7IuIQ4DMRcXFmLt8a+weZubWNDyBJC8ScrU6zYJemkJk/iIiLgO9n5g8j4gXATwG7IgJ6LTPfrGY/KyJeQ+/7dhRwAr1bJgNcNNvIJWnxmLPVdRbs0vQeqH4AAnhvZv6H/hki4jjg14CTM3NfRPwF8Ki+WX4wk0glSeZsdZajxEj1+BTwyog4HCAinhgRm4EfA/4Z+F5EHAmc2mKMkqQec7Y6xRZ2qQaZeWNE/DbwqYh4BHAA+D+BXfROpd4E3AFc3V6UkiQwZ6t7HCVGkiRJKphdYiRJkqSCWbBLkiRJBbNglyRJkgpmwS5JkiQVzIJdkiRJKpgFuyRJklQwC3ZJkiSpYBbskiRJUsH+f9eN7svDgvXjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time series\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(12,5))\n",
    "ax1.plot(f_cpi, 'o')\n",
    "ax1.set_ylim(-1,1)\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel(r'$F_{CPI}$')\n",
    "\n",
    "ax2.plot(f_ip, 'o')\n",
    "ax2.set_ylim(-2,2)\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel(r'$F_{IP}$')\n",
    "\n",
    "fig.savefig('../output/f_cpi_ip.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regression results for CPI and IP\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(12,5))\n",
    "ax1.plot(cpi_comb['CPI_1'], cpi_comb['CPI'], 'o')\n",
    "ax1.plot(cpi_comb['CPI_1'], beta_cpi_0 + beta_cpi_1*cpi_comb['CPI_1'], 'r', label='Best Fit')\n",
    "ax1.set_xlabel(r'$CPI\\ (t-1)$')\n",
    "ax1.set_ylabel(r'$CPI\\ (t)$')\n",
    "ax1.set_title('Consumer Price Index (CPI)')\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(ip_comb['IndProd_1'], ip_comb['IndProd'], 'o')\n",
    "ax2.plot(ip_comb['IndProd_1'], beta_ip_0 + beta_ip_1*ip_comb['IndProd_1'], 'r', label='Best Fit')\n",
    "ax2.set_xlabel(r'$IP\\ (t-1)$')\n",
    "ax2.set_ylabel(r'$IP\\ (t)$')\n",
    "ax2.set_title('Industrial Production (IP)')\n",
    "ax2.legend()\n",
    "\n",
    "fig.savefig('../output/cpi_ip_regression.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL_E</th>\n",
       "      <th>F_CPI</th>\n",
       "      <th>F_IP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>-15.559467</td>\n",
       "      <td>-0.265428</td>\n",
       "      <td>0.322638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-28</th>\n",
       "      <td>-2.577849</td>\n",
       "      <td>-0.297135</td>\n",
       "      <td>-0.270980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-31</th>\n",
       "      <td>0.285049</td>\n",
       "      <td>-0.128474</td>\n",
       "      <td>0.004985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-30</th>\n",
       "      <td>0.027105</td>\n",
       "      <td>0.085452</td>\n",
       "      <td>0.135454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-31</th>\n",
       "      <td>2.224061</td>\n",
       "      <td>0.025388</td>\n",
       "      <td>-0.557601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AAPL_E     F_CPI      F_IP\n",
       "Date                                     \n",
       "2013-01-31 -15.559467 -0.265428  0.322638\n",
       "2013-02-28  -2.577849 -0.297135 -0.270980\n",
       "2013-03-31   0.285049 -0.128474  0.004985\n",
       "2013-04-30   0.027105  0.085452  0.135454\n",
       "2013-05-31   2.224061  0.025388 -0.557601"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get excess returns of Apple\n",
    "ret_excess_aapl = (df['AAPL'] - df['RF']).rename('AAPL_E')\n",
    "comb_df = pd.concat([ret_excess_aapl, f_cpi, f_ip], axis=1)\n",
    "comb_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate the time series factors for IP and CPI using Apple's excess returns:\n",
    "\n",
    "$R_{i,t}^e = b_{i,CPI}F_{CPI,t} + b_{i,IP}F_{IP,t}$ where $F_{CPI,t}$ and $F_{IP,t}$ denote the time series for two factors: CPI and IP, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>AAPL_E</td>      <th>  R-squared:         </th> <td>   0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 10 Feb 2020</td> <th>  Prob (F-statistic):</th>  <td> 0.261</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:56:59</td>     <th>  Log-Likelihood:    </th> <td> -276.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    81</td>      <th>  AIC:               </th> <td>   559.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    78</td>      <th>  BIC:               </th> <td>   566.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    1.4354</td> <td>    0.834</td> <td>    1.721</td> <td> 0.089</td> <td>   -0.225</td> <td>    3.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>F_CPI</th>     <td>   -2.7057</td> <td>    4.948</td> <td>   -0.547</td> <td> 0.586</td> <td>  -12.556</td> <td>    7.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>F_IP</th>      <td>    2.6849</td> <td>    1.693</td> <td>    1.586</td> <td> 0.117</td> <td>   -0.685</td> <td>    6.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.564</td> <th>  Durbin-Watson:     </th> <td>   2.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.062</td> <th>  Jarque-Bera (JB):  </th> <td>   5.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.597</td> <th>  Prob(JB):          </th> <td>  0.0820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.236</td> <th>  Cond. No.          </th> <td>    5.93</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 AAPL_E   R-squared:                       0.034\n",
       "Model:                            OLS   Adj. R-squared:                  0.009\n",
       "Method:                 Least Squares   F-statistic:                     1.365\n",
       "Date:                Mon, 10 Feb 2020   Prob (F-statistic):              0.261\n",
       "Time:                        09:56:59   Log-Likelihood:                -276.70\n",
       "No. Observations:                  81   AIC:                             559.4\n",
       "Df Residuals:                      78   BIC:                             566.6\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      1.4354      0.834      1.721      0.089      -0.225       3.096\n",
       "F_CPI         -2.7057      4.948     -0.547      0.586     -12.556       7.144\n",
       "F_IP           2.6849      1.693      1.586      0.117      -0.685       6.055\n",
       "==============================================================================\n",
       "Omnibus:                        5.564   Durbin-Watson:                   2.036\n",
       "Prob(Omnibus):                  0.062   Jarque-Bera (JB):                5.001\n",
       "Skew:                          -0.597   Prob(JB):                       0.0820\n",
       "Kurtosis:                       3.236   Cond. No.                         5.93\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the regression\n",
    "model = sm.ols('AAPL_E ~ F_CPI + F_IP', data=comb_df).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get model results\n",
    "intercept = model.params['Intercept']\n",
    "coef_cpi = model.params['F_CPI']\n",
    "coef_ip = model.params['F_IP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "prediction = intercept + coef_cpi*f_cpi + coef_ip*f_ip\n",
    "err = (prediction-comb_df['AAPL_E'])*100/comb_df['AAPL_E']\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(12,5))\n",
    "ax1.plot(prediction, 'o', label='Prediction')\n",
    "ax1.plot(comb_df['AAPL_E'], 'o', label='Actual')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Excess Returns of APPL')\n",
    "ax1.set_ylim(-25,25)\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(err, 'o')\n",
    "ax2.set_ylim(-300,200)\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('% Error in Prediction')\n",
    "fig.savefig('../output/percent_error_pred.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a19e99f28>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.plot(prediction, 'o', label='Prediction')\n",
    "ax.plot(comb_df['AAPL_E'], 'o', label='Actual')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
